Sauna intelligent steam control system, method, computing device, and storage medium

Abstract

The application discloses a sauna room intelligent steam control system and method, a computing device and a computer storage medium, according to the technical scheme provided by the application, the sauna room intelligent steam control system comprises: an information acquisition module, which is used for collecting state parameters of a sauna room in real time through a sensor; an adjusting signal generation module, which is used for performing data analysis on the state parameters and preset parameters, and generating an adjusting signal according to an analysis result; and a control module, which controls a sauna stove and / or a water outlet valve according to the adjusting signal, and adjusts a steam output temperature of the sauna stove and / or a water flow of the water outlet valve; the collected state parameters of the sauna room and the preset parameters are compared and analyzed through the application, and then an adjustment mode capable of making the state parameters reach the preset parameters is determined, and the control of the sauna stove and the water outlet valve is completed; therefore, the temperature and humidity requirements of users in the sauna room can be quickly met, the whole adjustment process is more scientific and environmentally friendly, and efficiency and energy saving and water saving are taken into account.

Description

Technical Field

[0001] This invention relates to the field of bathroom equipment, and in particular to an intelligent steam control system, method, computing device, and computer storage medium for sauna rooms. Background Technology

[0002] With the rapid development of my country's social economy and the gradual improvement of people's economic level, the demand for comfort in the bathing process has also increased. At the same time, people's life pressure is also increasing, and many choose sauna baths for high-temperature sweating to improve blood circulation, relax joints, and enhance blood vessel elasticity, thus achieving rest, relaxation, and even physical therapy. Currently, saunas are mostly located in public bathhouses or sports venues such as swimming pools. However, as people's fondness for and acceptance of sauna baths increases, many families are also choosing to install saunas in their homes.

[0003] Saunas require a high temperature (approximately 45-60℃) and high humidity environment to achieve the aforementioned effects. Whether located in a public bathhouse or a home, saunas typically employ two methods to maintain temperature and humidity. The first is manually pouring water onto the sauna stove (high-temperature rocks) to increase the amount of steam. However, this method is prone to significant fluctuations in temperature and humidity due to the difficulty in precisely controlling the timing and amount of water, resulting in poor user comfort. The second method involves continuously adding water to the sauna stove to maintain temperature and humidity, but this method can lead to excessively high temperatures and humidity, and wastes resources. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an intelligent steam control system for sauna rooms, a corresponding intelligent steam control method for sauna rooms, a computing device, and a computer storage medium.

[0005] According to one aspect of the present invention, a smart steam control system for a sauna is provided, comprising:

[0006] The information acquisition module is used to collect the status parameters of the sauna room in real time through sensors;

[0007] The adjustment signal generation module is used to perform data analysis on state parameters and preset parameters, and generate adjustment signals based on the analysis results.

[0008] The control module controls the sauna oven and / or water outlet valve according to the adjustment signal, adjusting the steam output temperature of the sauna oven and / or the water flow rate of the water outlet valve.

[0009] In the above solution, the intelligent steam control system for the sauna room is characterized in that,

[0010] The status parameters include at least: ambient temperature, ambient humidity, steam output temperature, and water flow rate.

[0011] The preset parameters include at least: preset temperature parameters and preset humidity parameters.

[0012] In the above scheme, the adjustment signal generation module further includes: a judgment submodule; wherein,

[0013] The judgment submodule is used to calculate the temperature difference between the preset temperature parameter and the ambient temperature parameter, and the humidity difference between the preset humidity parameter and the ambient humidity parameter, based on the status parameters and preset parameters.

[0014] Based on the status parameters and preset parameters, it is determined in real time whether the adjustment conditions are met; if the adjustment conditions are met, it means that the sauna boiler and / or water outlet valve need to be adjusted; otherwise, no adjustment is required; among which,

[0015] The adjustment conditions are: the ambient temperature parameter is lower than the preset temperature parameter and the temperature difference is greater than the preset temperature difference, and / or the ambient humidity parameter is lower than the preset humidity parameter and the humidity difference is greater than the preset humidity difference.

[0016] In the above scheme, when it is necessary to adjust the sauna stove and the water outlet valve, the adjustment signal generation module is further used for:

[0017] Using AI algorithms, based on preset parameters, status parameters, sauna boiler rated power, and maximum water output of the outlet valve, the optimal adjustment method to raise the status parameters to the preset parameters is determined.

[0018] Generate adjustment signals according to the optimal adjustment method.

[0019] In the above scheme, the recognition module is used to acquire user facial information, preprocess the user facial information, and extract preset parameters corresponding to the user facial information;

[0020] The identification module includes:

[0021] The first acquisition submodule is used for:

[0022] The system acquires user portrait information, performs grayscale conversion on the user portrait information to obtain a grayscale image, and acquires the grayscale value of each pixel in the grayscale image.

[0023] The first average gray level is calculated based on the gray level value of each pixel in the grayscale image.

[0024] The first filtering submodule is used for:

[0025] Calculate the ratio of the gray value of each pixel in the grayscale image to the first grayscale mean;

[0026] Pixels with a ratio greater than a first preset threshold are selected as first interference pixels. All pixels in the grayscale image are traversed to obtain the first interference pixel set.

[0027] Pixels whose ratio is less than the second preset threshold are selected as the second interference pixels. All pixels in the grayscale image are traversed to obtain the set of second interference pixels. The first preset threshold is greater than the second preset threshold.

[0028] The second filtering submodule is used for:

[0029] Take any pixel in the grayscale image, and determine the first region to be processed with that pixel as the center and the first preset step size as the radius.

[0030] The second grayscale mean is calculated based on the grayscale values ​​of each pixel in the first region to be processed.

[0031] Calculate the ratio of the gray value of each pixel in the first region to be processed to the average gray value of the second region;

[0032] Pixels with a ratio greater than the first preset threshold are selected as third interference pixels. All pixels in the grayscale image are traversed to obtain the set of third interference pixels.

[0033] Pixels with a ratio less than the second preset threshold are selected as the fourth interference pixels. All pixels in the grayscale image are traversed to obtain the set of the fourth interference pixels.

[0034] The third filtering submodule is used for:

[0035] Find the intersection of the first set of interfering pixels and the third set of interfering pixels to determine the fifth set of interfering pixels;

[0036] Find the intersection of the second set of interfering pixels and the fourth set of interfering pixels to determine the sixth set of interfering pixels;

[0037] The noise reduction submodule is used for:

[0038] Take any fifth interference pixel from the set of fifth interference pixels, and determine the second region to be processed in the grayscale image with the fifth interference pixel as the center and the second preset step size as the radius.

[0039] Based on the gray values ​​of each pixel in the second region to be processed, the third gray mean value is calculated, and the gray value of the fifth interference pixel is replaced with the third gray mean value; based on the above method, all interference points in the fifth interference pixel set are replaced to obtain the first correction set;

[0040] Take any sixth interference pixel from the set of sixth interference pixels, and determine the third region to be processed in the grayscale image with the sixth interference pixel as the center and the second preset step size as the radius.

[0041] Based on the gray values ​​of each pixel in the third region to be processed, the fourth gray value is calculated, and the gray value of the sixth interference pixel is replaced with the fourth gray value. Based on the above method, all interference points in the sixth interference pixel set are replaced to obtain the second correction set.

[0042] Based on the first correction set and the second correction set, the corrected image is obtained;

[0043] The enhancement submodule is used to enhance the corrected image to obtain the enhanced image.

[0044] Preset parameters corresponding to the user's portrait information are extracted from the enhanced image.

[0045] In the above scheme, the enhancement submodule includes:

[0046] The second acquisition submodule is used to separate the foreground and background of the corrected image to obtain the foreground grayscale image and the background grayscale image.

[0047] The first enhancement submodule is used for:

[0048] The foreground grayscale image is denoised using Gaussian filtering to obtain the denoised image;

[0049] Calculate the difference between the pixels in the foreground grayscale image and the corresponding pixels in the denoised image;

[0050] If the difference is greater than the first preset difference, then the corresponding pixel is enhanced based on the first preset enhancement coefficient;

[0051] If the difference is less than the second preset difference, then the corresponding pixel is enhanced based on the second preset enhancement coefficient; the second preset difference is less than the first preset difference.

[0052] Based on the above steps, the foreground grayscale image enhancement process is completed to obtain the first enhanced image;

[0053] The second enhancement submodule is used for:

[0054] Obtain the grayscale value of each pixel in the background grayscale image and calculate the fifth grayscale mean value, and compare the fifth grayscale mean value with a preset grayscale threshold;

[0055] If the fifth grayscale mean is less than the preset grayscale threshold, the background image is magnified based on the first preset magnification factor;

[0056] If the fifth grayscale mean is greater than or equal to the preset grayscale threshold, then calculate the difference between the fifth grayscale mean and the preset grayscale threshold.

[0057] If the difference is greater than a preset grayscale difference threshold, the background image is magnified based on a second preset magnification factor;

[0058] If the difference is less than or equal to a preset grayscale difference threshold, the background image is magnified based on a third preset magnification factor; wherein the first preset magnification factor, the second preset magnification factor, and the third preset magnification factor decrease sequentially.

[0059] The background grayscale image is enhanced in brightness according to the magnification factor of each pixel to obtain the image to be processed; the image to be processed is compressed at a preset resolution to obtain the second enhanced image; wherein, the preset resolution is the product of the resolution of the image to be processed and a preset scaling factor.

[0060] The image enhancement output submodule is used to merge the first enhanced image and the second enhanced image to obtain an enhanced image and output it.

[0061] According to another aspect of the present invention, a method for intelligent steam control in a sauna room is provided, employing the aforementioned intelligent steam control system for sauna rooms, comprising:

[0062] The sauna room's status parameters are collected in real time using sensors;

[0063] Perform data analysis on state parameters and preset parameters, and generate adjustment signals based on the analysis results;

[0064] Adjust the signal to control the sauna oven and / or the water outlet valve, and adjust the steam output temperature of the sauna oven and / or the water flow rate of the water outlet valve.

[0065] In the above scheme, the state parameters include at least: ambient temperature parameters, ambient humidity parameters, steam output temperature parameters, and water flow rate parameters;

[0066] The preset parameters include at least: preset temperature parameters and preset humidity parameters.

[0067] The above-mentioned intelligent steam control method for sauna rooms further includes:

[0068] Acquire user facial image information, detect and analyze the user facial image information, and extract preset parameters corresponding to the user facial image information.

[0069] According to another aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0070] The memory is used to store at least one executable instruction, which causes the processor to perform operations corresponding to the above-described intelligent steam control method for sauna rooms.

[0071] According to another aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing a processor to perform operations corresponding to the above-described intelligent steam control method for sauna rooms.

[0072] According to the technical solution provided by this invention, an intelligent steam control system for sauna rooms includes: an information acquisition module for real-time acquisition of sauna room status parameters via sensors; an adjustment signal generation module for data analysis of the status parameters and preset parameters, and generation of adjustment signals based on the analysis results; and a control module for controlling the sauna furnace and / or water outlet valve according to the adjustment signals, adjusting the steam output temperature of the sauna furnace and / or the water flow rate of the water outlet valve. By acquiring the current temperature and humidity parameters of the sauna room through sensors and comparing them with parameters preset by the user to meet their comfort requirements, it determines whether the sauna furnace needs to be turned on or adjusted, effectively avoiding adjustments to the sauna furnace when the temperature and humidity are still within the comfortable range, greatly reducing resource waste. When adjustment is needed, an AI algorithm is used to derive a scientific adjustment method, and the sauna furnace is further adjusted accordingly to ensure the temperature and humidity within the sauna room. This further ensures the rationality and scientific nature of the adjustment method, effectively reducing energy and water waste, and efficiently maintaining the temperature and humidity within the user's comfort range, greatly improving the user experience.

[0073] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0074] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0075] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0076] Figure 1 An intelligent steam control system for a sauna room according to an embodiment of the present invention is shown;

[0077] Figure 2 A method for intelligent steam control in a sauna according to an embodiment of the present invention is shown;

[0078] Figure 3 A method for determining and generating signals for sauna steam control according to an embodiment of the present invention is shown;

[0079] Figure 4 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. Detailed Implementation

[0080] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0081] Figure 1 An intelligent steam control system for a sauna room according to an embodiment of the present invention is shown. The system includes: an information acquisition module 100, a regulation signal generation module 200, and a control module 300; wherein,

[0082] The information acquisition module 100 is used to collect the status parameters of the sauna room in real time through sensors.

[0083] Specifically, the state parameters include at least: ambient temperature parameters, ambient humidity parameters, steam output temperature parameters, and water flow rate parameters.

[0084] Preferably, the ambient temperature parameter is the temperature parameter of the user rest area in the sauna room; the ambient humidity parameter is the humidity parameter of the user rest area in the sauna room; the steam output temperature parameter is the temperature parameter at the steam outlet of the sauna furnace; and the water flow rate parameter is the water flow rate at the water outlet valve used to generate steam in the sauna furnace.

[0085] The adjustment signal generation module 200 is used to perform data analysis on state parameters and preset parameters, and determine and generate adjustment signals based on the analysis results.

[0086] Specifically, the preset parameters include at least: preset temperature parameters and preset humidity parameters.

[0087] Preferably, the preset parameters are the system's default temperature and humidity parameters, or the preset parameters are temperature and humidity parameters set by the user to meet their own comfort needs.

[0088] Specifically, the adjustment signal generation module 200 further includes: a judgment submodule 201; wherein,

[0089] The judgment submodule 201 is used to calculate the temperature difference between the preset temperature parameter and the ambient temperature parameter, and the humidity difference between the preset humidity parameter and the ambient humidity parameter, based on the status parameters and preset parameters.

[0090] Based on the status parameters and preset parameters, it is determined in real time whether the adjustment conditions are met; if the adjustment conditions are met, it means that the sauna boiler and / or water outlet valve need to be adjusted; otherwise, no adjustment is required; among which,

[0091] The adjustment conditions are: the ambient temperature parameter is lower than the preset temperature parameter and the temperature difference is greater than the preset temperature difference, and / or the ambient humidity parameter is lower than the preset humidity parameter and the humidity difference is greater than the preset humidity difference.

[0092] Specifically, the adjustment signal generation module 200 is further used to use an AI algorithm to determine the optimal adjustment method to raise the state parameters to the preset parameters based on preset parameters, state parameters, rated power of the sauna furnace, and maximum water output of the water outlet valve; and generate an adjustment signal according to the optimal adjustment method.

[0093] To increase the temperature and humidity to the preset levels, the quickest method is to use the sauna stove at its rated power and open the water valve to maximum flow. This allows for a rapid increase in both temperature and humidity, but it also leads to some waste. For example, with an electrically heated sauna stove, this results in wasted electricity and water. Therefore, it's advisable to appropriately reduce the sauna stove's power and the water flow rate to achieve temperature and humidity adjustment more quickly while minimizing energy and water consumption.

[0094] For example, if the ambient temperature is 35℃, the preset temperature is 50℃, the ambient humidity is 80%, the preset humidity is 100%, the sauna heater's rated power is 10kW, and the maximum water flow rate of the outlet valve is 360L / h; if it takes 15 minutes to raise the status parameters to the preset parameters based on the sauna heater's rated power and the outlet valve's maximum water flow rate, and 25 minutes with a power of 3kW and a water flow rate of 120L / h, then according to these values, the preset parameters can still be achieved more economically in a relatively shorter time. Relatively speaking, the latter is a better adjustment method. The allowable time range can be set by the user, such as twice the minimum adjustment time.

[0095] Preferably, the AI ​​algorithm determines, based on the above parameters, the optimal adjustment method for the power required for heating the sauna furnace and the water flow rate of the water outlet valve during the process of raising the ambient temperature and humidity to the preset temperature and humidity, so as to complete the temperature and humidity raising process in a more energy-efficient manner within the allowable time range. The AI ​​algorithm can employ algorithms such as simulated annealing, which are not limited to this invention.

[0096] Preferably, if either the temperature or humidity in the sauna room reaches the preset parameter requirement first, the corresponding adjustment method will be stopped or reduced first. For example, if the ambient temperature parameter reaches the preset temperature parameter requirement first, the sauna heater can be stopped or its power reduced, while the water flow from the outlet valve can be maintained temporarily; if the ambient humidity parameter reaches the preset humidity parameter requirement first, the outlet valve can be closed, while the power of the sauna heater can be maintained temporarily.

[0097] The control module 300 controls the sauna oven and / or the water outlet valve according to the adjustment signal, adjusting the steam output temperature of the sauna oven and / or the water flow rate of the water outlet valve.

[0098] Preferably, the intelligent steam control system for the sauna room further includes:

[0099] The recognition module is used to acquire user facial information, preprocess the user facial information, and extract preset parameters corresponding to the user facial information;

[0100] The identification module includes:

[0101] The first acquisition submodule is used for:

[0102] The system acquires user portrait information, performs grayscale conversion on the user portrait information to obtain a grayscale image, and acquires the grayscale value of each pixel in the grayscale image.

[0103] The first average gray level is calculated based on the gray level value of each pixel in the grayscale image.

[0104] The first filtering submodule is used for:

[0105] Calculate the ratio of the gray value of each pixel in the grayscale image to the first grayscale mean;

[0106] Pixels with a ratio greater than a first preset threshold are selected as first interference pixels. All pixels in the grayscale image are traversed to obtain the first interference pixel set.

[0107] Pixels whose ratio is less than the second preset threshold are selected as the second interference pixels. All pixels in the grayscale image are traversed to obtain the set of second interference pixels. The first preset threshold is greater than the second preset threshold.

[0108] The second filtering submodule is used for:

[0109] Take any pixel in the grayscale image, and determine the first region to be processed with that pixel as the center and the first preset step size as the radius.

[0110] The second grayscale mean is calculated based on the grayscale values ​​of each pixel in the first region to be processed.

[0111] Calculate the ratio of the gray value of each pixel in the first region to be processed to the average gray value of the second region;

[0112] Pixels with a ratio greater than the first preset threshold are selected as third interference pixels. All pixels in the grayscale image are traversed to obtain the set of third interference pixels.

[0113] Pixels with a ratio less than the second preset threshold are selected as the fourth interference pixels. All pixels in the grayscale image are traversed to obtain the set of the fourth interference pixels.

[0114] The third filtering submodule is used for:

[0115] Find the intersection of the first set of interfering pixels and the third set of interfering pixels to determine the fifth set of interfering pixels;

[0116] Find the intersection of the second set of interfering pixels and the fourth set of interfering pixels to determine the sixth set of interfering pixels;

[0117] The noise reduction submodule is used for:

[0118] Take any fifth interference pixel from the set of fifth interference pixels, and determine the second region to be processed in the grayscale image with the fifth interference pixel as the center and the second preset step size as the radius.

[0119] Based on the gray values ​​of each pixel in the second region to be processed, the third gray mean value is calculated, and the gray value of the fifth interference pixel is replaced with the third gray mean value; based on the above method, all interference points in the fifth interference pixel set are replaced to obtain the first correction set;

[0120] Take any sixth interference pixel from the set of sixth interference pixels, and determine the third region to be processed in the grayscale image with the sixth interference pixel as the center and the second preset step size as the radius.

[0121] Based on the gray values ​​of each pixel in the third region to be processed, the fourth gray value is calculated, and the gray value of the sixth interference pixel is replaced with the fourth gray value. Based on the above method, all interference points in the sixth interference pixel set are replaced to obtain the second correction set.

[0122] Based on the first correction set and the second correction set, the corrected image is obtained;

[0123] The enhancement submodule is used to enhance the corrected image to obtain the enhanced image.

[0124] Preset parameters corresponding to the user's portrait information are extracted from the enhanced image.

[0125] In this embodiment, a grayscale image is an image form that contains only brightness information but not color information. It is usually used to represent black and white images or to convert color images into black and white images. In a grayscale image, the value of each pixel is an integer between 0 and 255, where 0 represents black and 255 represents white.

[0126] In this embodiment, grayscale value refers to the color depth of a point in a black and white image, typically ranging from 0 to 255; this range corresponds to all gray shades between white (full brightness) and black (no brightness); the higher the grayscale value, the brighter the color; the lower the grayscale value, the darker the color.

[0127] In this embodiment, the image is denoised. The first step of the denoising process is to screen out noise points or interference points in the image. By screening out the noise points or interference points, the target pixels for image denoising are determined. By denoising the target pixels, the denoising process of the entire image is achieved. Using this denoising scheme accelerates the image denoising process and improves the image denoising effect.

[0128] In this embodiment, the first interference pixel is the pixel in the entire grayscale image whose grayscale value to the first grayscale mean is greater than the first preset threshold. The main purpose is to filter out noise pixels with relatively large grayscale values.

[0129] In this embodiment, the second interference pixel is the pixel whose gray value to the first gray mean is less than the second preset threshold in the entire grayscale image, mainly filtering out noise pixels with relatively small gray values.

[0130] In this embodiment, the first preset threshold is greater than the second preset threshold, mainly as a standard for noise point detection and judgment. Different preset thresholds are set according to the requirements of image processing under different conditions to improve the noise processing effect.

[0131] In this embodiment, a first region to be processed is determined. A relatively small screening range is determined with an arbitrary pixel as the center and a first preset step size as the radius. This is mainly to improve the screening accuracy of noise points. All pixels are traversed to perform a second precise screening of interference points.

[0132] In this embodiment, the first grayscale mean and the second grayscale mean are different. The first grayscale mean is the grayscale mean of the entire grayscale image, and the second grayscale mean is the grayscale mean of the first region to be processed.

[0133] In this embodiment, the third interference pixel is the pixel in the first image to be processed whose gray value to the second gray average value ratio is greater than the first preset threshold. The main purpose is to filter out noise pixels with relatively large gray values ​​in the first image to be processed.

[0134] In this embodiment, the fourth interference pixel is the pixel in the first image to be processed whose gray value to the second gray mean is less than the second preset threshold. The main purpose is to filter out noise pixels with relatively small gray values ​​in the first image to be processed.

[0135] In this embodiment, the first preset threshold is the same for both pixel screenings, and the second preset threshold is the same for both screenings. The same screening conditions are more conducive to improving the accuracy of judging interference points and provide a guarantee for the accurate determination of subsequent interference points.

[0136] In this embodiment, the first preset step size and the second preset step size have different values. In different image processing environments, different values ​​are taken according to different needs. The specific size relationship between the first preset step size and the second preset step size is not defined here.

[0137] In this embodiment, the gray value of the fifth interfering pixel is replaced with the third gray-scale mean to achieve noise reduction of the fifth interfering pixel, and the gray value of the sixth interfering pixel is replaced with the fourth gray-scale mean to achieve noise reduction of the sixth interfering pixel.

[0138] In this embodiment, the corrected image is the image after noise reduction.

[0139] In this embodiment, enhancing the image after noise reduction is more conducive to subsequent image recognition and extraction.

[0140] The above techniques offer the following benefits: Grayscale processing reduces the amount of information in an image, thereby increasing processing speed. In a grayscale image, the grayscale value represents the brightness of each pixel, reflecting different brightness levels. Therefore, grayscale processing allows for a clear visualization of noise distribution and intensity within the image. By comparing grayscale images before and after denoising, the denoising effect is readily apparent. Performing two rounds of interference point screening on the grayscale image at different ranges improves the accuracy of interference point identification, which is beneficial for subsequent denoising of interfering pixels. The first interference point screening is performed based on the entire grayscale image, taking into account the entire grayscale image... Using the first grayscale mean as the criterion, each pixel is traversed to complete the first screening of interference points. If it is only generally determined that the interference in the first screening is the final interference point, it is obviously not accurate enough. Therefore, in order to improve accuracy, the interference points are screened a second time. The second screening adopts the local judgment method. Specifically, a pixel in the grayscale image is randomly selected, and the first processing area is determined with the pixel as the center and the first preset step size as the radius. The entire grayscale image is divided into the first processing area, and interference pixels are screened from each area to select the third set of interference pixels and the fourth set of interference pixels, thus completing the second screening of interference pixels. Two different filtering results are obtained by filtering the interfering pixels twice. A third filtering method is then applied to these two results: finding the intersection of the first and third sets of interfering pixels to determine the fifth set, and finding the intersection of the second and fourth sets to determine the sixth set. These intersection results identify the final set of interfering pixels, thus completing the identification of the interference pixels. By filtering the interference pixels, the computational load of image processing is further reduced, completing the transformation from surfaces to points and improving the efficiency of image processing. Once the interfering pixels are identified, the next step is to perform noise reduction processing on these pixels. The gray value of the fifth interfering pixel is replaced by calculating the third gray-level mean of the second processing area. The gray value of the sixth interfering pixel is replaced by calculating the fourth gray-level mean of the third processing area. The first and second correction sets are then completed, and the corrected image is obtained using these sets. This image noise reduction method achieves three advantages: first, it reduces the computational load in the image processing process by converting surfaces into points; second, it improves the accuracy of identifying interfering pixels by determining the final interfering pixels through three rounds of screening; and third, it enhances the noise reduction effect.

[0141] Preferably, the enhancement submodule includes:

[0142] The second acquisition submodule is used to separate the foreground and background of the corrected image to obtain the foreground grayscale image and the background grayscale image.

[0143] The first enhancement submodule is used for:

[0144] The foreground grayscale image is denoised using Gaussian filtering to obtain the denoised image;

[0145] Calculate the difference between the pixels in the foreground grayscale image and the corresponding pixels in the denoised image;

[0146] If the difference is greater than the first preset difference, then the corresponding pixel is enhanced based on the first preset enhancement coefficient;

[0147] If the difference is less than the second preset difference, then the corresponding pixel is enhanced based on the second preset enhancement coefficient; the second preset difference is less than the first preset difference.

[0148] Based on the above steps, the foreground grayscale image enhancement process is completed to obtain the first enhanced image;

[0149] The second enhancement submodule is used for:

[0150] Obtain the grayscale value of each pixel in the background grayscale image and calculate the fifth grayscale mean value, and compare the fifth grayscale mean value with a preset grayscale threshold;

[0151] If the fifth grayscale mean is less than the preset grayscale threshold, the background image is magnified based on the first preset magnification factor;

[0152] If the fifth grayscale mean is greater than or equal to the preset grayscale threshold, then calculate the difference between the fifth grayscale mean and the preset grayscale threshold.

[0153] If the difference is greater than a preset grayscale difference threshold, the background image is magnified based on a second preset magnification factor;

[0154] If the difference is less than or equal to a preset grayscale difference threshold, the background image is magnified based on a third preset magnification factor; wherein the first preset magnification factor, the second preset magnification factor, and the third preset magnification factor decrease sequentially.

[0155] The background grayscale image is enhanced in brightness according to the magnification factor of each pixel to obtain the image to be processed; the image to be processed is compressed at a preset resolution to obtain the second enhanced image; wherein, the preset resolution is the product of the resolution of the image to be processed and a preset scaling factor.

[0156] The image enhancement output submodule is used to merge the first enhanced image and the second enhanced image to obtain an enhanced image and output it.

[0157] In this embodiment, the image is corrected by separating the foreground and background. Separating the foreground and background can help us better process and understand the information in the image. By separating the foreground from the background, image enhancement can be performed more effectively.

[0158] In this embodiment, the foreground grayscale image is denoised using Gaussian filtering. Noise may be generated during the process of separating the foreground and background of the image, so the foreground grayscale image is denoised.

[0159] In this embodiment, the foreground grayscale image is enhanced by a preset enhancement coefficient to better highlight the foreground target and improve its visibility and recognizability. By applying the enhancement coefficient to the foreground grayscale image, the contrast and brightness of the image can be adjusted, increasing the contrast of the foreground target and making it more prominent, thereby improving the target's visibility and recognizability. At the same time, the enhancement coefficient can also suppress background noise and interference, reducing the impact of the background on target recognition and detection, further improving the target's recognizability and accuracy.

[0160] In this embodiment, the first enhancement coefficient and the second enhancement coefficient are determined based on the statistical characteristics of the image. For example, the average gray value and standard deviation of the image can be calculated, and the enhancement coefficient can be selected based on these statistical measures.

[0161] First, the image is preprocessed to convert it into a grayscale image, and then the average grayscale value of the grayscale image is calculated.

[0162] Secondly, when calculating the standard deviation of an image, the relative position and gray value of each pixel need to be considered. The standard deviation reflects the image's contrast and dynamic range.

[0163] Finally, an enhancement coefficient can be determined based on the average gray value and the standard deviation. For example, a threshold can be set: when the standard deviation is less than a certain threshold, the enhancement coefficient is 1; when the standard deviation is greater than a certain threshold, the enhancement coefficient is the standard deviation divided by the threshold; when the standard deviation is between two thresholds, the enhancement coefficient can be linearly interpolated.

[0164] This method is simple, but in practical applications, more factors may need to be considered, such as the local characteristics of the image and noise; in addition, the choice of enhancement coefficient also needs to be adjusted according to the specific situation.

[0165] In this embodiment, the magnification factor of each pixel is determined based on the average grayscale value of each pixel and a preset grayscale threshold, which can be achieved through the following steps:

[0166] First, according to the definition of gray mean, the gray mean of all pixels in the image is calculated as gray_mean.

[0167] Next, based on a preset grayscale threshold, the grayscale value of each pixel in the image is compared with that threshold. Let's assume the grayscale value of a pixel in the image is gray_pixel.

[0168] If gray_pixel is less than the grayscale threshold, set its magnification factor to a preset magnification factor, such as 2x or 3x.

[0169] If gray_pixel is greater than or equal to the grayscale threshold, proceed to the next step.

[0170] Calculate the difference between gray_pixel and the gray mean (gray_pixel-gray_mean). If the difference is greater than a preset threshold (e.g., 0.5), then set the magnification of that pixel to the preset magnification.

[0171] If the difference is less than or equal to the preset threshold, the magnification factor of the pixel is set to 1.

[0172] By following the steps above, the magnification factor for each pixel can be determined, thereby further enhancing the image.

[0173] In this embodiment, enhancing the background grayscale image with magnification can improve image contrast: magnification enhances image contrast, making image details clearer and easier to observe and analyze; reduce image noise: magnification increases the size of the original image, thereby reducing noise in the image. This is because after image magnification, the originally small noise becomes smaller relative to the whole image, thus appearing less noticeable visually; enhance detail features: magnification helps highlight detailed features in the image, making these features easier to identify and understand, which is particularly important for applications that need to extract and analyze detailed information in images; facilitate subsequent processing: magnification makes the image easier to process for subsequent operations, such as image segmentation, feature extraction, and object detection. These operations typically require a larger pixel space and higher resolution; magnification provides more pixel information, thereby improving the accuracy and stability of the operation.

[0174] In this embodiment, compression is performed at different resolutions because signals at different resolutions have different characteristics. Specifically, the original signal is decomposed into pyramids of different resolutions, each containing a certain number of wavelet coefficients, which can better capture the different features of the signal. For the lower resolution sub-bands, smoothing and downsampling can be performed to reduce noise and data redundancy. At the same time, for the higher resolution sub-bands, more refined processing and enhancement can be performed to extract more details and features, thereby enabling better analysis and processing.

[0175] In this embodiment, the preset resolution is the product of the resolution of the image to be processed and the preset scaling factor, and the preset scaling factor ranges from 0.2 to 0.8.

[0176] In this embodiment, the enhanced image is obtained by merging the first enhanced image and the second enhanced image.

[0177] The above techniques offer the following benefits: Foreground and background separation of the corrected image helps us better process and understand image information; by separating the foreground and background, image enhancement can be performed more effectively. Noise reduction is applied to the foreground grayscale image using Gaussian filtering, as noise may be generated during foreground and background separation. The difference between the pixels in the foreground grayscale image and the corresponding pixels in the denoised image is calculated and compared with a first preset difference and a second preset difference. Based on the comparison result, enhancement is performed using a first preset enhancement coefficient and a second preset enhancement coefficient to obtain the first enhanced image. Image enhancement of the foreground grayscale image using preset enhancement coefficients aims to better highlight the foreground target and improve its visibility and recognizability. Applying enhancement coefficients to the foreground grayscale image adjusts the image's contrast and brightness, increasing the contrast of the foreground target and making it more prominent, thereby improving its visibility and recognizability. Simultaneously, the enhancement coefficients can suppress background noise and interference, reducing the background's influence on target recognition and detection, further improving the target's visibility and recognizability. The image enhancement scheme in this embodiment first separates the foreground and background grayscale images. The foreground grayscale image is enhanced based on an enhancement coefficient, while the background grayscale image is scaled and enhanced. This more refined enhancement provides convenient conditions for subsequent image feature extraction and recognition. The extracted features are more accurate, facilitating a better service experience in the practical application of intelligent steam control systems for sauna rooms, thus having a positive and practical effect.

[0178] Preferably, the obtained user portrait information is compared with the portrait information already stored in the portrait information database; if the comparison is successful, the preset parameters corresponding to the user portrait information are determined according to the comparison result; if the comparison fails, the input mode is opened so that the user can input the preset parameters himself, or the default preset parameters are automatically adopted.

[0179] Preferably, a facial recognition device can be installed in the sauna room to acquire user facial information through a camera; the facial information database and the preset parameters corresponding to the user facial information can be stored in the recognition module 400; or they can be stored in a cloud storage terminal, and the recognition module 400 can retrieve them from the cloud storage terminal via a wireless network to complete the facial comparison and preset parameter determination.

[0180] The intelligent steam control system for sauna rooms provided in this embodiment includes: an information acquisition module for real-time acquisition of sauna room status parameters via sensors; an adjustment signal generation module for data analysis of status parameters and preset parameters, and generation of adjustment signals based on the analysis results; and a control module for controlling the sauna furnace and / or water outlet valve according to the adjustment signals, adjusting the steam output temperature of the sauna furnace and / or the water flow rate of the water outlet valve. By acquiring the current temperature and humidity parameters of the sauna room through sensors installed at various locations within the sauna room, and comparing them with parameters preset by the user to meet their comfort requirements, the system determines whether it is necessary to turn on the sauna furnace and / or water outlet valve for temperature and humidity adjustment. This effectively avoids adjusting the sauna furnace when the temperature and humidity are still within the comfortable range, greatly reducing resource waste. When adjustments are needed, AI algorithms are used to derive scientific adjustment methods, which are then used to further adjust the sauna stove to ensure the temperature and humidity inside the sauna room. This ensures the rationality and scientific nature of the adjustment methods, effectively reducing energy and water waste and efficiently maintaining the temperature and humidity within the user's comfort range. At the same time, facial recognition adaptively obtains preset parameters from different users, automatically adjusting the parameters inside the sauna room according to the user's needs without user intervention, greatly improving the user experience.

[0181] Figure 2 A method for intelligent steam control in a sauna according to an embodiment of the present invention is shown, such as... Figure 2 As shown, the method employs the aforementioned intelligent steam control system for sauna rooms and includes the following steps:

[0182] Step S201: Collect the status parameters of the sauna room in real time through sensors.

[0183] Specifically, the state parameters include at least: ambient temperature parameters, ambient humidity parameters, steam output temperature parameters, and water flow rate parameters.

[0184] Step S202: Perform data analysis on the state parameters and preset parameters, and determine the generation of adjustment signals based on the analysis results.

[0185] Specifically, the preset parameters include at least: preset temperature parameters and preset humidity parameters.

[0186] Preferably, before step S201, the method may further include acquiring user portrait information, detecting and analyzing the user portrait information, and extracting preset parameters corresponding to the user portrait information.

[0187] Preferably, the obtained user portrait information is compared with the portrait information already stored in the portrait information database; if the comparison is successful, the preset parameters corresponding to the user portrait information are determined according to the comparison result; if the comparison fails, the input mode is opened so that the user can input the preset parameters himself, or the default preset parameters are automatically adopted.

[0188] Preferably, a facial recognition device can be installed in the sauna room to acquire user facial information through a camera; the facial information database and the preset parameters corresponding to the user facial information can be stored locally; or they can be stored in a cloud storage terminal and retrieved from the cloud storage terminal via a wireless network to complete the facial comparison and preset parameter determination.

[0189] Step S203: Adjust the signal to control the sauna oven and / or the water outlet valve, and adjust the steam output temperature of the sauna oven and / or the water flow rate of the water outlet valve.

[0190] According to the intelligent steam control method for sauna rooms provided in this embodiment, sensors collect the state parameters of the sauna room in real time; the state parameters and preset parameters are analyzed, and an adjustment signal is generated based on the analysis results; the adjustment signal controls the sauna furnace and / or the water outlet valve, adjusting the steam output temperature of the sauna furnace and / or the water flow rate of the water outlet valve. Through the intelligent steam control method for sauna rooms provided in this embodiment, by comparing and analyzing the collected sauna room state parameters with preset parameters, the adjustment method that can bring the state parameters to the preset parameters is determined, thus completing the control of the sauna furnace and the water outlet valve. Therefore, it can quickly meet the user's temperature and humidity requirements in the sauna room, keeping the temperature and humidity within the user's comfortable range, greatly improving the user experience, and making the entire adjustment process more scientific and environmentally friendly, taking into account efficiency and energy and water conservation.

[0191] Figure 3 A method for determining and generating signals for sauna steam control according to an embodiment of the present invention is shown, such as... Figure 3 As shown, the method includes the following steps:

[0192] Step S301: Calculate the temperature difference between the preset temperature parameter and the ambient temperature parameter, and the humidity difference between the preset humidity parameter and the ambient humidity parameter, based on the status parameters and preset parameters.

[0193] Step S302: Based on the status parameters and preset parameters, determine in real time whether the adjustment conditions are met.

[0194] Specifically, if yes, it means that the sauna stove and / or water outlet valve need to be adjusted, and step S303 is executed; if no, it means that no adjustment is needed, and the process returns to step S301.

[0195] Preferably, the adjustment conditions are that the ambient temperature parameter is lower than the preset temperature parameter and the temperature difference is greater than the preset temperature difference, and / or that the ambient humidity parameter is lower than the preset humidity parameter and the humidity difference is greater than the preset humidity difference.

[0196] Step S303: Using an AI algorithm, based on preset parameters, status parameters, sauna boiler rated power, and maximum water output of the outlet valve, determine the optimal adjustment method to raise the status parameters to the preset parameters.

[0197] Step S304: Generate an adjustment signal according to the optimal adjustment method.

[0198] Based on the above method, the relationship between state parameters and preset parameters can be used to determine whether the sauna heater needs to be activated. The heater is only activated and adjusted when the adjustment conditions are met, effectively avoiding adjustments when the temperature and humidity are still within the comfortable range, thus significantly reducing resource waste. When adjustment is needed, an AI algorithm is used to derive a scientific adjustment method, and the sauna heater is adjusted accordingly to ensure the temperature and humidity within the sauna room. This further ensures the rationality and scientific nature of the adjustment method, effectively reducing energy and water waste and efficiently maintaining temperature and humidity within the user's comfortable range.

[0199] The present invention also provides a non-volatile computer storage medium storing at least one executable instruction that can execute the intelligent steam control method for sauna rooms in any of the above method embodiments.

[0200] Figure 4 The diagram illustrates the structure of a computing device according to an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0201] like Figure 4 As shown, the computing device may include: a processor 402, a communications interface 404, a memory 406, and a communications bus 408.

[0202] in:

[0203] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408.

[0204] Communication interface 404 is used to communicate with other network elements such as clients or other servers.

[0205] The processor 402 is used to execute program 410, specifically to perform the relevant steps in the above-described embodiment of the intelligent steam control method for sauna rooms.

[0206] Specifically, program 410 may include program code, which includes computer operation instructions.

[0207] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0208] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0209] Specifically, program 410 can be used to cause processor 402 to execute the intelligent steam control method for sauna rooms in any of the above method embodiments. The specific implementation of each step in program 410 can be found in the corresponding descriptions of the steps and units in the above embodiments of the intelligent steam control method for sauna rooms, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0210] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0211] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0212] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0213] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0214] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0215] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0216] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A smart steam control system for a sauna, comprising: The information acquisition module is used to collect the status parameters of the sauna room in real time through sensors; The adjustment signal generation module is used to perform data analysis on state parameters and preset parameters, and generate adjustment signals based on the analysis results. The control module controls the sauna oven and / or water outlet valve according to the adjustment signal, and adjusts the steam output temperature of the sauna oven and / or the water flow rate of the water outlet valve; Also includes: The recognition module is used to acquire user facial information, preprocess the user facial information, and extract preset parameters corresponding to the user facial information; The identification module includes: The first acquisition submodule is used for: The system acquires user portrait information, performs grayscale conversion on the user portrait information to obtain a grayscale image, and acquires the grayscale value of each pixel in the grayscale image. The first average gray level is calculated based on the gray level value of each pixel in the grayscale image. The first filtering submodule is used for: Calculate the ratio of the gray value of each pixel in the grayscale image to the first grayscale mean; Pixels with a ratio greater than a first preset threshold are selected as first interference pixels. All pixels in the grayscale image are traversed to obtain the first interference pixel set. Pixels whose ratio is less than the second preset threshold are selected as the second interference pixels. All pixels in the grayscale image are traversed to obtain the set of second interference pixels. The first preset threshold is greater than the second preset threshold. The second filtering submodule is used for: Take any pixel in the grayscale image, and determine the first region to be processed with that pixel as the center and a first preset step size as the radius. The second grayscale mean is calculated based on the grayscale values ​​of each pixel in the first region to be processed. Calculate the ratio of the gray value of each pixel in the first region to be processed to the average gray value of the second region; Pixels with a ratio greater than the first preset threshold are selected as third interference pixels. All pixels in the grayscale image are traversed to obtain the set of third interference pixels. Pixels with a ratio less than the second preset threshold are selected as the fourth interference pixels. All pixels in the grayscale image are traversed to obtain the set of the fourth interference pixels. The third filtering submodule is used for: Find the intersection of the first set of interfering pixels and the third set of interfering pixels to determine the fifth set of interfering pixels; Find the intersection of the second set of interfering pixels and the fourth set of interfering pixels to determine the sixth set of interfering pixels; The noise reduction submodule is used for: Take any fifth interference pixel from the set of fifth interference pixels, and determine the second region to be processed in the grayscale image with the fifth interference pixel as the center and the second preset step size as the radius. Based on the gray values ​​of each pixel in the second region to be processed, the third gray mean value is calculated, and the gray value of the fifth interference pixel is replaced with the third gray mean value; based on the above method, all interference points in the fifth interference pixel set are replaced to obtain the first correction set; Take any sixth interference pixel from the set of sixth interference pixels, and determine the third region to be processed in the grayscale image with the sixth interference pixel as the center and the second preset step size as the radius. Based on the gray values ​​of each pixel in the third region to be processed, the fourth gray value is calculated, and the gray value of the sixth interference pixel is replaced with the fourth gray value. Based on the above method, all interference points in the sixth interference pixel set are replaced to obtain the second correction set. Based on the first correction set and the second correction set, the corrected image is obtained; The enhancement submodule is used to enhance the corrected image to obtain the enhanced image. Preset parameters corresponding to the user's portrait information are extracted from the enhanced image.

2. The intelligent steam control system for sauna rooms according to claim 1, characterized in that, The adjustment signal generation module further includes: a judgment submodule; wherein, The judgment submodule is used to calculate the temperature difference between the preset temperature parameter and the ambient temperature parameter, and the humidity difference between the preset humidity parameter and the ambient humidity parameter, based on the status parameters and preset parameters. Based on the status parameters and preset parameters, it is determined in real time whether the adjustment conditions are met; if the adjustment conditions are met, it means that the sauna boiler and / or water outlet valve need to be adjusted; otherwise, no adjustment is required; among which, The adjustment conditions are: the ambient temperature parameter is lower than the preset temperature parameter and the temperature difference is greater than the preset temperature difference, and / or the ambient humidity parameter is lower than the preset humidity parameter and the humidity difference is greater than the preset humidity difference.

3. The intelligent steam control system for sauna rooms according to claim 1, characterized in that, When it is necessary to adjust the sauna stove and water outlet valve, the adjustment signal generation module is further used for: Using AI algorithms, based on preset parameters, status parameters, sauna boiler rated power, and maximum water output of the outlet valve, the optimal adjustment method to raise the status parameters to the preset parameters is determined. Generate adjustment signals according to the optimal adjustment method.

4. The intelligent steam control system for sauna rooms according to claim 1, characterized in that, The enhancement submodule includes: The second acquisition submodule is used to separate the foreground and background of the corrected image to obtain the foreground grayscale image and the background grayscale image. The first enhancement submodule is used for: The foreground grayscale image is denoised using Gaussian filtering to obtain the denoised image; Calculate the difference between the pixels in the foreground grayscale image and the corresponding pixels in the denoised image; If the difference is greater than the first preset difference, then the corresponding pixel is enhanced based on the first preset enhancement coefficient; If the difference is less than the second preset difference, then the corresponding pixel is enhanced based on the second preset enhancement coefficient; the second preset difference is less than the first preset difference. Based on the above steps, the foreground grayscale image enhancement process is completed to obtain the first enhanced image; The second enhancement submodule is used for: Obtain the grayscale value of each pixel in the background grayscale image and calculate the fifth grayscale mean value, and compare the fifth grayscale mean value with a preset grayscale threshold; If the fifth grayscale mean is less than the preset grayscale threshold, the background image is magnified based on the first preset magnification factor; If the fifth grayscale mean is greater than or equal to the preset grayscale threshold, then calculate the difference between the fifth grayscale mean and the preset grayscale threshold. If the difference is greater than a preset grayscale difference threshold, the background image is magnified based on a second preset magnification factor; If the difference is less than or equal to a preset grayscale difference threshold, the background image is magnified based on a third preset magnification factor; wherein the first preset magnification factor, the second preset magnification factor, and the third preset magnification factor decrease sequentially. The background grayscale image is enhanced in brightness according to the magnification factor of each pixel to obtain the image to be processed; the image to be processed is compressed at a preset resolution to obtain the second enhanced image; wherein, the preset resolution is the product of the resolution of the image to be processed and a preset scaling factor. The image enhancement output submodule is used to merge the first enhanced image and the second enhanced image to obtain an enhanced image and output it.

5. A method for intelligent steam control in a sauna, characterized in that, The sauna room intelligent steam control system according to claim 1 includes: The sauna room's status parameters are collected in real time using sensors; Perform data analysis on state parameters and preset parameters, and generate adjustment signals based on the analysis results; Adjust the signal to control the sauna oven and / or the water outlet valve, and adjust the steam output temperature of the sauna oven and / or the water flow rate of the water outlet valve.

6. The method according to claim 5, characterized in that, The method further includes: The status parameters include at least: ambient temperature, ambient humidity, steam output temperature, and water flow rate. The preset parameters include at least: preset temperature parameters and preset humidity parameters.

7. The method according to claim 5, characterized in that, The intelligent steam control method for sauna rooms also includes: Acquire user facial image information, detect and analyze the user facial image information, and extract preset parameters corresponding to the user facial image information.

8. A computing device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the function corresponding to the intelligent steam control system for sauna rooms as described in any one of claims 1-4.

9. A computer storage medium storing at least one executable instruction that causes a processor to perform the function corresponding to the intelligent steam control system for sauna rooms as described in any one of claims 1-4.

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